import pandas as pd
import numpy as np
import plotly.express as px
import polars as pl
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Define custom color palette
colors = ["#0081a7", "#00afb9", "#fdfcdc", "#fed9b7", "#f07167"]The First Step: Mapping a Child’s Right
Education is the doorway to opportunity, yet millions are still left standing outside that door.Around the world, school attendance rates tell a powerful story — a story of inclusion, struggle, and progress.To begin, let’s take a global view. Where are children making it into the classroom — and where are they being left behind?
fig = px.choropleth(
latest_df,
locations="alpha_3_code",
color="obs_value",
hover_name="country",
color_continuous_scale=palette,
title=f"School Attendance Rate - {latest_year}"
)
fig.update_layout(margin={"r":0,"t":50,"l":0,"b":0})
fig.show()This map paints a striking picture: Some nations shine brightly, nearing full enrollment, while others reveal heartbreaking gaps, reminding us of the unevenness in access to learning.
While access has improved, gender disparities remain in many regions. How do boys’ and girls’ school attendance rates compare?
male = df[df["sex"] == "Male"]
female = df[df["sex"] == "Female"]
male_latest = male[male["time_period"] == latest_year][["country", "obs_value"]].rename(columns={"obs_value": "Male"})
female_latest = female[female["time_period"] == latest_year][["country", "obs_value"]].rename(columns={"obs_value": "Female"})
gender_df = pd.merge(male_latest, female_latest, on="country")
plt.figure(figsize=(8,6))
sns.regplot(data=gender_df, x="Male", y="Female", scatter_kws={"s":50, "alpha":0.6}, line_kws={"color": palette[4]})
plt.grid(True, linestyle="--", alpha=0.6)
plt.tight_layout()
plt.show()
In an ideal world, every dot would fall perfectly on the diagonal line — signaling equality. Yet reality shows us deviations:
In some nations, girls still lag behind.
In others, boys are increasingly vulnerable to dropout.
Understanding these differences is crucial for shaping targeted, inclusive education policies.
Progress isn’t measured only by snapshots — but by journeys.Take, for instance, Afghanistan. A nation marked by decades of turmoil, yet striving to move forward.
afg = df_total[df_total["country"] == "Afghanistan"]
plt.figure(figsize=(8,6))
sns.lineplot(data=afg, x="time_period", y="obs_value", marker="o", color=palette[7])
plt.grid(True, linestyle="--", alpha=0.6)
plt.tight_layout()
plt.show()
While fluctuations remain, the overall picture reveals resilience.Every year of improvement represents thousands more children learning, dreaming, and building toward a different future.
Beyond individual nations, we can observe the collective movement of countries making major strides.
top3 = latest_df.sort_values("obs_value", ascending=False).head(3)["country"]
area_df = df_total[df_total["country"].isin(top3)]
plt.figure(figsize=(10,6))
for i, c in enumerate(top3):
sub = area_df[area_df["country"] == c]
plt.fill_between(sub["time_period"], sub["obs_value"], alpha=0.5, color=palette[i], label=c)
plt.legend()
plt.grid(True, linestyle="--", alpha=0.5)
plt.tight_layout()
plt.show()
This area chart shows not only how top-performing countries have improved — but also the sheer momentum behind educational growth when communities, governments, and families come together.It reminds us that while the starting points differ, progress is possible everywhere.
Behind every data point we have seen lies a child’s story —A story of hope, perseverance, and potential.Education is not a luxury.It is the engine of healthier societies, stronger economies, and more peaceful nations.As we visualize these global patterns, we must remember:Each additional child stepping into a classroom brings the entire world closer to a brighter tomorrow.